Reproduce “The Autodidactic Universe” on OpenResearch

About this paper

Stephon Alexander, William J. Cunningham, Jaron Lanier, Lee Smolin, Stefan Stanojevic, Michael W. Toomey, Dave Wecker

A theoretical framework developed by researchers from Brown University, Perimeter Institute, and Microsoft Research posits that the universe learns its own physical laws through autodidactic processes. It establishes a formal correspondence between cubic matrix models, fundamental physical theories (like Plebanski gravity), and neural network architectures, demonstrating how laws could dynamically emerge and adapt.

Key insights

• Fundamental physical theories, particularly gauge and gravitational theories, can be formally mapped onto and interpreted as the dynamics of learning machines like neural networks.
• Autodidactic learning, where systems define their own learning criteria, provides a plausible mechanism for the universe to learn its laws without external supervision.
• Operational irreversibility can arise in learning systems even if underlying physical laws are reversible, due to the prohibitively vast memory needed to track and reverse complex learning processes.

Summary by alphaXiv.